Simon Schug
@smonsays.bsky.social
postdoc @princeton computational cognitive science ∪ machine learning https://smn.one
LLM agents are a serious problem for online experiments. It is very easy to use them and very hard to spot them. What can researchers do? With @brendenlake.bsky.social, we suggest detecting LLMs based on their lack of human cognitive constraints in our #CogSci2026 paper: arxiv.org/abs/2604.00016
The hardest part of science is posing the right question, not answering it.
M. Nolan Gray 🥑@mnolangray.bsky.social · 4mo agoWhat's something that experts/practitioners in your field universally agree upon, but that remains a "hot take" among the general public?
Excited that my paper on metacontrol in humans and neural networks with @summerfieldlab.bsky.social and @lhuntneuro.bsky.social is out in PNAS! We examine the way that predictive representations of control enable behavioral adaptation across settings, and pathologies: www.pnas.org/doi/10.1073/...
I think Fodor & Pylyshyn's 1988 paper is possibly the most mischaracterized paper in the history of cognitive science. It's often cited as arguing that neural networks cannot achieve systematicity, compositionality, and productivity. But that's not what they actually argue...
Our experiences have countless details, and it can be hard to know which matter. How can we behave effectively in the future when, right now, we don't know what we'll need? Out today in @nathumbehav.nature.com , @marcelomattar.bsky.social and I find that people solve this by using episodic memory.
Episodic memory facilitates flexible decision-making via access to detailed events - Nature Human Behaviour
Nicholas and Mattar found that people use episodic memory to make decisions when it is unclear what will be needed in the future. These findings reveal how the rich representational capacity of episod...
nature.com
Last term I tried an experiment: I walked into my Tech and Design Ethics class, admitted that I had *no idea* what to do about ChatGPT - so I would let them figure it out. As in: their first project was to decide and write the ChatGPT policy for the class. Here's what happened:
Excited to announce a new book telling the story of mathematical approaches to studying the mind, from the origins of cognitive science to modern AI! The Laws of Thought will be published in February and is available for pre-order now.
Thrilled to start 2026 as faculty in Psych & CS @ualberta.bsky.social + Amii.ca Fellow! 🥳 Recruiting students to develop theories of cognition in natural & artificial systems 🤖💭🧠. Find me at #NeurIPS2025 workshops (speaking coginterp.github.io/neurips2025 & organising @dataonbrainmind.bsky.social)
I am recruiting graduate students for the experimental side of my lab @mcgill.ca for admission in Fall 2026! Get in touch if you're interested in how brain circuits implement distributed computation, including dopamine-based distributed RL and probabilistic representations.
Does scaling lead to compositional generaliztation? Our #NeurIPS2025 Spotlight paper suggests that it can -- with the right training distribution. 🧵 A short thread:
I'm joining Princeton University as an Associate Professor of Computer Science and Psychology this fall! Princeton is ambitiously investing in AI and Natural & Artificial Minds, and I'm excited for my lab to contribute. Recruiting postdocs and Ph.D. students in CS and Psychology — join us!
Are transformers smarter than you? Hypernetworks might explain why. Come checkout our Oral at #ICLR tomorrow (Apr 26th, poster at 10:00, Oral session 6C in the afternoon). openreview.net/forum?id=V4K...
LLMs have shown impressive performance in some reasoning tasks, but what internal mechanisms do they use to solve these tasks? In a new preprint, we find evidence that abstract reasoning in LLMs depends on an emergent form of symbol processing arxiv.org/abs/2502.20332 (1/N)
Emergent Symbolic Mechanisms Support Abstract Reasoning in Large Language Models
Many recent studies have found evidence for emergent reasoning capabilities in large language models, but debate persists concerning the robustness of these capabilities, and the extent to which they ...
arxiv.org
For my first Bluesky post, I'm very excited to share a thread on our recent work with Mitra Javadzadeh, investigating how connections between cortical areas shape computations in the neocortex! [1/7] www.biorxiv.org/content/10.1...
Dynamic consensus-building between neocortical areas via long-range connections
The neocortex is organized into functionally specialized areas. While the functions and underlying neural circuitry of individual neocortical areas are well studied, it is unclear how these regions op...
biorxiv.org
Pre-print 🧠🧪 Is mechanism modeling dead in the AI era? ML models trained to predict neural activity fail to generalize to unseen opto perturbations. But mechanism modeling can solve that. We say "perturbation testing" is the right way to evaluate mechanisms in data-constrained models 1/8
Cutting it a bit fine, but here’s my review of the year in neuroscience for 2024 The eighth of these, would you believe? We’ve got dark neurons, tiny monkeys, the most complete brain wiring diagram ever constructed, and much more… Published on The Spike Enjoy! medium.com/the-spike/20...
2024: A Review of the Year in Neuroscience
Feeling a bit wired
medium.com
I wrote an introduction to RL for neuroscience last year that was just published in NBDT: tinyurl.com/5f58zdy3 This review aims to provide some intuition for and derivations of RL methods commonly used in systems neuroscience, ranging from TD learning through the SR to deep and distributional RL!
An introduction to reinforcement learning for neuroscience | Published in Neurons, Behavior, Data analysis, and Theory
By Kristopher T. Jensen. Reinforcement learning for neuroscientists
tinyurl.com
Stitching component models into system models has proven difficult in biology. But how much easier has it been in engineering? www.argmin.net/p/monster-mo...
Monster Models
Systems-level biology is hard because systems-level engineering is hard.
argmin.net
🚨 New Paper! Can neuroscience localizers uncover brain-like functional specializations in LLMs? 🧠🤖 Yes! We analyzed 18 LLMs and found units mirroring the brain's language, theory of mind, and multiple demand networks! w/ @gretatuckute.bsky.social, @abosselut.bsky.social, @mschrimpf.bsky.social 🧵👇
1/ Okay, one thing that has been revealed to me from the replies to this is that many people don't know (or refuse to recognize) the following fact: The unts in ANN are actually not a terrible approximation of how real neurons work! A tiny 🧵. 🧠📈 #NeuroAI #MLSky
Blake Richards@tyrellturing.bsky.social · 2y agoWhy does anyone have any issue with this? I've seen people suggesting it's problematic, that neuroscientists won't like it, and so on. But, I literally don't see why this is problematic...
For my first post on Bluesky .. I'll start by announcing our 2025 edition of EEML which will be in Sarajevo :) ! I'm really excited about it and hope to see many of you there. Please follow the website (and Bluesky account) for more details which are coming soon ..
EEML@eemlcommunity.bsky.social · 2y agoHello Bluesky! 🦋 This will be the official account of the Eastern European Machine Learning (EEML) community. Follow us for news regarding our summer schools, workshops, education/community initiatives, and more!
Have you had private doubts whether we'll ever understand the brain? Whether we'll be able explain psychological phenomena in an exhaustive way that ranges from molecules to membranes to synapses to cells to cell types to circuits to computation to perception and behavior?
What counts as in-context learning (ICL)? Typically, you might think of it as learning a task from a few examples. However, we’ve just written a perspective (arxiv.org/abs/2412.03782) suggesting interpreting a much broader spectrum of behaviors as ICL! Quick summary thread: 1/7
The broader spectrum of in-context learning
The ability of language models to learn a task from a few examples in context has generated substantial interest. Here, we provide a perspective that situates this type of supervised few-shot learning...
arxiv.org
Thrilled to share our NeurIPS Spotlight paper with Jan Bauer*, @aproca.bsky.social*, @saxelab.bsky.social, @summerfieldlab.bsky.social, Ali Hummos*! openreview.net/pdf?id=AbTpJ... We study how task abstractions emerge in gated linear networks and how they support cognitive flexibility.
Great thread from @michaelhendricks.bsky.social! Reminds me of something Larry Abbott once said to me at a summer school: Many physicists come into neuroscience assuming that the failure to find laws of the brain was just because biologists aren't clever enough. In fact, there are no laws. 🧠📈 🧪
Michael Hendricks 🇨🇦@michaelhendricks.bsky.social · 2y agoI came across a quote in an article, which I will paraphrase: the ultimate goal of neuroscience is to model the brain and derive laws that define the brain’s computational abilities. Statements like this are common and presented as self-evident, but I think they are wrong.
(1/5) Very excited to announce the publication of Bayesian Models of Cognition: Reverse Engineering the Mind. More than a decade in the making, it's a big (600+ pages) beautiful book covering both the basics and recent work: mitpress.mit.edu/978026204941...
To help find people at the intersection of neuroscience and AI. Of course let me know if I missed someone or you’d like to be added 🧪 🧠 #neuroskyence go.bsky.app/CAfmKQs
